Image segmentation is an initial step in many image processing methods such as feature extraction, pattern recognition, image enhancement, and noise reduction. One type of image segmentation called amplitude segmentation separates an image into segments having similar amplitude (luminance) employing a luminance threshold operation. See "Digital Image Processing" by W. K. Pratt, pp. 534-538, John Wiley & Sons, 1978. Often luminance thresholding is not adequate to segment an image, and other information such as edge information has been used to improve the image segmentation. See for example "Image Segmentation by Pixel Classification in (Gray Level, Edge Value) Space" by D. P. Panda and A. Rosenfeld. Abdou and Pratt have outlined a statistical procedure for segmenting an image signal. See the artical "Qaulitative Design and Evaluation of Enhancement/Threshold Edge Detectors" Proceedings of the IEEE, Vol. 67, No. 5, May 19, 1979, page 753-763. At page 756 of the article the authors suggest employing a maximum likelihood ratio test on the pixel values of an image for the hypothesis that an edge exists at any given pixel, based upon a statistical model of the edge probabilities in the image. However, the authors do not derive actual operating equations for the purposed method due to two difficulties which they duly note. First, there does not exist a single edge model for any large class of images of interest, and therefore, one must have a prior knowledge of the edge probabilities for a limited class of images to which the image to be processed belongs. The second difficulty is that for many complex edge models, no analytical solution exists to derive the maximum likelihood estimators employed in the maximum likelihood ratio test. Because of these problems, after mentioning the possiblity of a statistical approach to image segmentation, the authors turn their attention to techniques employing matched filters to perform segmentation. A problem with these prior art image segmentation methods is that they require as inputs ad hoc threshold levels, and require that empirical assumptions be made about the nature of the images that are to be segmented. In addition, statistical techniques that are based on pixel values can achieve at best two-pixel resolution because such techniques require independent sample sets and since both the mean and variance can change within an image, each sample set requires two pixels to estimate the two unknowns. It is an object of the present invention to provide an image segmentation method that requires a minimum of ad hoc inputs, and produces a more accurate segmentation for all classes of images of interest to a human observer.